Accurate estimation of the structure of a roadway ahead of a vehicle is an important component in autonomous driving and computer vision-based driving assistance. Using computer vision techniques to provide assistance while driving, instead of mechanical sensors, allows for the use of the information that is recorded for use in estimating vehicle movement to also be used in estimating ego-motion identifying lanes and the like, without the need for calibration between sensors as would be necessary with mechanical sensors. This reduces cost and maintenance.
There are several problems in determining the structure of a roadway. Typically, roads have few feature points, if any. The most obvious features in a road, such as lane markings, are often difficult to detect and have a generally linear structure, whereas background image structures, such as those associated with other vehicles, buildings, trees, and the like, will typically have many feature points. This will make image- or optical-flow-based estimation difficult in practice. In addition, typically images that are recorded for roadway structure estimation will contain a large amount of “outlier” information that is either not useful in estimating roadway structure, or that may result in poor estimation. For example, in estimating of roadway structure, images of objects such as other vehicles will contribute false information for the road structure estimation. In addition, conditions that degrade image quality, such as raindrops and glare, will also make accurate road structure estimation difficult.